An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks

As intelligent transportation systems (ITS) approach the realm of widespread deployment, there is an increasing need to robustly capture the variability of link travel time in real-time to generate reliable predictions of real-time traffic conditions. This study proposes an adaptive information fusion model to predict the short-term link travel time distribution by iteratively combining past information on link travel time on the current day with the real-time link travel time information available at discrete time points. The past link travel time information is represented as a discrete distribution. The real-time link travel time is represented as a range, and is characterized using information quality in terms of information accuracy and time delay. A nonlinear programming formulation is used to specify the adaptive information fusion model to update the short-term link travel time distribution by focusing on information quality. The model adapts good information by weighing it higher while shielding the effects of bad information by reducing its weight. Numerical experiments suggest that the proposed model adequately represents the short-term link travel time distribution in terms of accuracy and robustness, while ensuring consistency with ambient traffic flow conditions. Further, they illustrate that the mean of a representative short-term travel time distribution is not necessarily a good tracking indicator of the actual (ground truth) time-dependent travel time on that link. Parametric sensitivity analysis illustrates that information accuracy significantly influences the model, and dominates the effects of time delay and the consistency constraint parameter. The proposed information fusion model bridges key methodological gaps in the ITS deployment context related to information fusion and the need for short-term travel time distributions.     

Understanding members' carsharing (activity) persistency by using econometric model

Carsharing is an innovative travel alternative that has recently experienced considerable growth and become part of sustainable transportation initiatives. Although carsharing is becoming increasingly a popular alternative transportation mode in North America, it is still an under‐researched area. Current research is aimed at better understanding of the behavior of carsharing users. For every member, a two‐stage approach microsimulates the probability of being active in any month using a binary probit model and given that a particular member is active during a month, the probability of that member using the service multiple times using a random utility‐based model. The model is estimated using empirical data from one of the largest carsharing companies in North America. The model estimates reveal that the activity persistency of members is positively linked to previous behaviors for up to 4 months, and that the influence of previous months weakens over time. It also shows that some attributes of the traveler (gender, age, and language spoken at home) impact his or her behaviors. Copyright © 2010 John Wiley & Sons, Ltd.     

A latent class accelerated hazard model of social activity duration

Over the past decade, activity scheduling processes have gained increasing attention in the field of transportation research. However, still little is known about the scheduling of social activities even though these activities account for a large and growing portion of trips. This paper contributes to this knowledge. We analyze how the duration of social activities is influenced by social activity characteristics and characteristics of the relationship between the respondent and the contacted person(s). To that end, a latent class accelerated hazard model is estimated, based on social interaction diary data that was collected in the Netherlands in 2008. Chi-square tests and analyses of variance are used to test for significant relations between the latent classes and personal and household characteristics. Findings suggest that the social activity characteristics and the characteristics of the relationship between the socializing persons are highly significant in explaining social activity duration. This shows that social activities should not be considered as a homogenous set of activities and it underlines the importance of including the social context in travel-behavior models. Moreover, the results indicate that there is a substantial amount of latent heterogeneity across the population. Four latent classes are identified, showing different social activity durations, and different effects for both categories of explanatory variables. Latent class membership can be explained by household composition, socio-economic status (education, income and work hours), car ownership and the number of interactions in 2 days.     

偏压连拱隧道施工方法研究

文章采川三维有限元数值模拟研究了偏压连拱隧道不同施工顺序下拱顶下沉、中墙稳定性及初期支护受力特征.研究结果表明,先开挖浅埋侧时,拱顶沉降较小,中墙在施工中的稳定安全系数较大、弯矩较小,初期支护受力较大;对于浅埋偏压连拱隧道,围岩变形及中墙在施工中的稳定性控制更为重要.所以,从有利于围岩变形、中墙稳定性控制以及中墙受力的角度出发,宜采用先开挖浅埋侧的施工方法.     

Logit模型在南广高铁线客流分担预测中的应用

对分担率进行科学的预测是交通运输规划的基础性工作,是制定运输决策的重要依据。文章经过比较选取多项Logit模型为分担率预测模型,介绍了模型建立与求解的方法,并运用该模型对南广高铁沿线客流分担率进行预测分析。     

管线敷设与风机室布置对综合管廊通风阻力影响研究

为准确估算管线敷设与风机室内气流相互作用对压降损失的影响,基于武汉市江夏区谭鑫培路地下综合管廊项目建立典型数值分析模型与分析流程。通过建立典型管线布置的GIL舱、高压舱与综合舱的计算流体动力学三维模型,分别对入口段与稳定段进行分析,并使用循环边界条件来考虑稳定段流体的充分发展,得到阻力系数随流量的变化规律;通过建立GIL舱典型排风口三维模型,分析风机室中不同风机的压降损失,并与规范估算值进行比较。研究结果表明： 管线敷设对压降损失的影响不可忽视;风机室风机相互作用显著,规范不能准确估算该相互作用导致的压降损失。     

盾构隧道管片接头嵌入式密封垫防水性能探究

为解决传统密封垫易于与混凝土管片胶结处发生渗漏水的问题,对新型嵌入式密封垫的防水性能进行研究。与传统的现场粘贴防水橡胶密封垫的工艺不同,嵌入式密封垫是将密封垫与混凝土管片整浇预制。采用模型试验和有限元数值模拟方法,对隧道接头中嵌入式密封垫的防水能力进行分析和论述。研究结果表明： 文中所涉截面形式的嵌入式密封垫在控制工况,即张开量7 mm、错缝量10 mm的情况下,满足上海深层排水调蓄隧道0.6 MPa的耐水压力要求,且弥补了传统密封垫易从密封垫与混凝土接触面渗漏水的缺陷。此外,通过在密封条两侧设置凹槽,可有效减小密封垫脚部与混凝土间接触应力,减少局部应力集中,保证混凝土管片的完整性,提高接头防水能力。     

地下交通转换平台射流通风特性及风流组织方式研究

为探究地下交通转换平台内通风系统的合理布局,采用比尺模型试验和CFD模拟相结合的方法,研究射流风机和通风组织对地下交通转换平台内气流运动的影响。结果表明： 1）当联络通道内风机射流朝向敞开段时,为使风机升压系数Kj最大,630 mm、900 mm、1 120 mm射流风机的布设位置应距离敞开段分别大于40、50、65 m; 2）大口径射流风机具有更大的Kj,但占用的断面空间更大,且射流诱导段更长,应根据联络通道长度和高度合理选择射流风机口径; 3）地下交通转换平台的通风组织不宜采用同侧开启方式,采用对角抽吸方式时,联络通道内的污染物混入比最低、通风效率最高。     

The value of options for time charterparty extension: an artificial neural networks (ANN) approach

The most frequently associated options in the physical shipping market are options to extend the charter period on time charters and additional shipment options on contracts of affreightment. The value of freight options, in practice, is estimated mostly by referring to forward curves. An option on freight has different properties from its financial counterparts, and the straightforward adoption of theoretical models does not produce promising results. In this paper, extension options, which have the property of options on futures, were transformed into regular European options before the application of the Black-Scholes model (BSM). The efficient market hypothesis, which justifies the parity of the performance of a long-term charter to that of repetitive short-term charters, worked as the basis for the transformation. The option values determined by the BSM were compared with actual realized values. Additionally, the artificial neural networks (ANN) was employed to derive the option values. This study is meaningful as the first-time application of both the closed-form solution and the ANN to the valuation of physical freight options. The research results can contribute to the quality of chartering decisions. The results could also be used in quantifying credit risk, as extension options tend to be granted to charterers with more creditability.     

基于HMM-SVR的船舶动力设备故障模式识别与状态预测研究

船舶动力设备因故障监测信号样本少、变化缓慢且数据特征呈非线性,使得设备故障模式的准确识别和状态预测比较难。鉴于此,文章研究了基于隐马尔科夫模型的故障模式识别方法,利用该模型将微弱变化的信号特征转换为变化较大的对数似然概率对故障模式实现有效识别。在此基础上进一步提出基于HMM-SVR的设备状态预测模型,将遗传算法用于支持向量回归模型参数寻优,并结合隐马尔科夫模型,实现对设备状态的预测。对船用柴油机进行仿真,结果表明上述模型具有较高的识别率,能准确预测船舶动力设备的当前状态。